@inproceedings{mrini-etal-2021-rewards,
    title = "Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization",
    author = "Mrini, Khalil  and
      Liu, Can  and
      Dreyer, Markus",
    editor = "Carenini, Giuseppe  and
      Cheung, Jackie Chi Kit  and
      Dong, Yue  and
      Liu, Fei  and
      Wang, Lu",
    booktitle = "Proceedings of the Third Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2021",
    address = "Online and in Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.newsum-1.4/",
    doi = "10.18653/v1/2021.newsum-1.4",
    pages = "33--38",
    abstract = "We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in Rouge, BERTScore, and human evaluation metrics."
}